Retrospective modeling outdoor air pollution at a fine spatial scale in France, 1989–2008

Bentayeb, Malek; Stempfelet, Morgane; Wagner, Vérène; Zins, Marie; Bonenfant, Sébastien; Songeur, Charlotte; Sanchez, Olivier; Rosso, Amandine; Brulfert, Guillaume; Rios, Isabelle; Chaxel, Eric; Virga, Jonathan; Armengaud, Alexandre; Rossello, Philippe; Rivière, Emmanuel; Bernard, M J; Vasbien, F.; Deprost, Raphaèle

doi:10.1016/j.atmosenv.2014.04.019

Cited by 27 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is carried out prospectively from the enrolment of the participants; we are also currently collecting and geocoding all the addresses of the 10 years before enrolment. This allows for assessing the residential exposure of the participants to various parameters available at an ecological scale, such as maps of air pollution41 and climate, electromagnetic fields from powerlines or exposure to radiofrequency fields from mobile phone base stations and broadcast transmitters, water pollution or noise. Residential histories can also be used to study available contextual socioeconomic parameters, such as urbanisation, population density, collective equipment,38 deprivation,42 or an index of accessibility to medical resources,43 already used by several nested research projects in CONSTANCES.…”

Section: What Has Been Measured?mentioning

confidence: 99%

CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile

et al. 2016

View full text Add to dashboard Cite

Why the cohort was set up?CONSTANCES is a general-purpose cohort with a focus on occupational and environmental factors.Cohort participantsCONSTANCES was designed as a randomly selected sample of French adults aged 18–69 years at inception; 200 000 participants will be included.Data collection phasesAt enrolment, the participants are invited to complete questionnaires and to attend a health screening centre (HSC) for a health examination. A biobank will be set up. The follow-up includes an yearly self-administered questionnaire, a periodic visit to an HSC and linkage to social and national health administrative databases.Main types of data collectedData collected for participants include social and demographic characteristics, socioeconomic status, life events and behaviours. Regarding occupational and environmental factors, a wealth of data on organisational, chemical, biological, biomechanical and psychosocial lifelong exposure, as well as residential characteristics, are collected at enrolment and during follow-up. The health data cover a wide spectrum: self-reported health scales, reported prevalent and incident diseases, long-term chronic diseases and hospitalisations, sick-leaves, handicaps, limitations, disabilities and injuries, healthcare usage and services provided, and causes of death.Control of selection effectsTo take into account non-participation and attrition, a random cohort of non-participants was set up and will be followed through the same national databases as participants.Data accessInclusions begun at the end of 2012 and more than 110 000 participants were already included by September 2016. Several projects on occupational and environmental risks already applied to a public call for nested research projects.

show abstract

Section: What Has Been Measured?mentioning

confidence: 99%

CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Annual concentrations of PM10, PM2.5, NO2, SO2, benzene and summer averages (15 April -15 September) of the daily maximum 8-hour concentrations of O3 were assessed at 2 km resolution throughout France from 1989 to 2008 using the CHIMERE chemistry-transport model, mesh refinement and data assimilation with geostatistical analyses. The modeling strategy is summarized in Figure 1 and has been previously described (Bentayeb et al 2014).…”

Section: Exposure Assessmentmentioning

confidence: 99%

“…The box plots describe the fifth percentile, first quartile, median, third quartile, ninety-fifth percentile of the distribution. Descriptive statistics of the discrepancy (bias, RMSE and correlation) between modelled and observed data (measurements from fixed monitoring stations) from 1989 to 2008 for PM10, PM2.5, NO2, O3, SO2, and benzene were previously detailed (Bentayeb et al 2014). Table 2 shows descriptive statistics of air pollution concentrations as mean ± standard deviation (SD), median and IQR, according to time lag exposure.…”

Section: Air Pollution Exposurementioning

confidence: 99%

Association between long-term exposure to air pollution and mortality in France: A 25-year follow-up study

Bentayeb

Wagner

Stempfelet

et al. 2015

Environment International

110

View full text Add to dashboard Cite

Long-term exposure to fine particles, nitrogen dioxide, sulfur dioxide and benzene is associated with an increased risk of non-accidental mortality in France. Our results strengthen existing evidence that outdoor air pollution is a significant environmental risk factor for mortality. Due to the limited sample size and the nature of our study (occupational), further investigations are needed in France with a larger representative population sample.

show abstract

“…‚ [P cN *E cp j´1q ] characterizes the population movement outside the study area;p CN is the probability that the population residing in census block N lived in location C (municipality or department or region) in year (j-1) E cp j´1q is the average exposure level of the location C in year (j-1) extracted from the meta-analysis of French studies conducted at the municipal and regional scales (e.g., Bentayeb et al, 2014).…”

Section: Theoretical Modelmentioning

confidence: 99%

A Conceptual Framework for the Assessment of Cumulative Exposure to Air Pollution at a Fine Spatial Scale

Talantikite¹,

Padilla²,

Zmirou‐Navier³

et al. 2016

ISEE Conference Abstracts

View full text Add to dashboard Cite

Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects' residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO 2 following two steps: (i) retrospective reconstitution of NO 2 concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10-to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs.

show abstract

Retrospective modeling outdoor air pollution at a fine spatial scale in France, 1989–2008

Cited by 27 publications

References 34 publications

CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile

CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile

Association between long-term exposure to air pollution and mortality in France: A 25-year follow-up study

A Conceptual Framework for the Assessment of Cumulative Exposure to Air Pollution at a Fine Spatial Scale

Contact Info

Product

Resources

About